22k citations · 25.1k across the 40 of their papers we have counts for
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Towards Accurate Generative Models of Video: A New Metric & Challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach +3
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and repre…
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered…
Investigating Object Compositionality in Generative Adversarial Networks
Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber +1
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations…
Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Raphaël Marinier +4
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create…
On Self Modulation for Generative Adversarial Networks
Ting Chen, Mario Lucic, Neil Houlsby +1
Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance acro…
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Hugo Penedones, Damien Vincent, Hartmut Maennel +3
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the sim…
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lucic, Xiaohua Zhai +2
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully appli…
On Accurate Evaluation of GANs for Language Generation
Stanislau Semeniuta, Aliaksei Severyn, Sylvain Gelly
Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metri…
Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic +2
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, suc…
Competitive Training of Mixtures of Independent Deep Generative Models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin +3
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervi…
MemGEN: Memory is All You Need
Sylvain Gelly, Karol Kurach, Marcin Michalski +1
We propose a new learning paradigm called Deep Memory. It has the potential to completely revolutionize the Machine Learning field. Surprisingly, this paradigm has not been reinven…
Gradient Descent Quantizes ReLU Network Features
Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solut…